A Prediction Model of Structural Settlement Based on EMD‐SVR‐WNN

Luo, Xizhao; Gan, Wenjuan; Wang, Lixin; Chen, Yonghong; Meng, Xue

doi:10.1155/2020/8831965

Cited by 10 publications

(7 citation statements)

References 27 publications

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“…Because this study focused on the safety and health issues of construction workers, a thorough manual screening process was conducted to further remove articles that did not fit into the research scope. Such removal was applied to articles that focus on the safety and health issues of workers in other industries [32,33], articles that deal with the safety and health issues of the structure but not workers [34,35], articles that discuss non-occupational safety and health issues [36,37], and articles that are not relevant at all. Abstracts of eight articles that were missing in the original dataset were manually filled in because the abstract of an article contains rich textual information for text mining, and the topic modeling technique generally performs better for large texts than for short texts.…”

Section: Data Collectionmentioning

confidence: 99%

Discovering the Research Topics on Construction Safety and Health Using Semi-Supervised Topic Modeling

et al. 2023

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Safety and health have been one of the major issues in the construction industry worldwide for decades, and the relevant research has correspondingly drawn much attention in the academic field. Considering the expanding size and increasing heterogeneity of this research field, this paper proposes the topic modeling approach to cluster latent topics, extract coherent keywords, and discover evolving trends over the past three decades. Focusing on a total of 1984 articles published in 27 different journal sources until February 2023, this paper applied both unsupervised topic modeling techniques—Latent Dirichlet Allocation (LDA) and Correlation Explanation (CorEx)—and their semi-supervised versions—Guided LDA and Anchored CorEx. The evolving trends and inter-relationship of 15 research topics generated by the Anchored CorEx model (the best-performing model) were analyzed. Top-listed documents of major topics were analyzed to discuss their standalone research focuses. The results of this paper provided helpful insights and implications of existing research and offered potential guides for future research on construction safety and health by helping researchers (1) select research topics of interest and clearing decaying topics; (2) extract the top words of each research topic using systematic approaches; and (3) explore the interconnection of different research topics as well as their standalone focuses.

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Section: Data Collectionmentioning

confidence: 99%

Discovering the Research Topics on Construction Safety and Health Using Semi-Supervised Topic Modeling

et al. 2023

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“…In order to predict the surface settlement caused by underground mining activities, Sepehri et al [7] established a threedimensional finite element model, and the average relative error between the effect and the measured data was 7.95%, which was in good agreement with the actual situation. Xianglong Luo et al [8] introduced Empirical Mode Decomposition (EMD) to process the non-stationary data in view of the non-stationary and nonlinear characteristics of the structural deformation data. Shaoyi Yang et al [9], in order to reduce the instability of short-term wind speed data and further improve the model prediction accuracy, processed historical data by Variational Mode Decomposition (VMD) and then further improved the prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Research on Deformation Prediction of VMD-GRU Deep Foundation Pit Based on PSO Optimization Parameters

Liu,

Zhang,

Jiang

et al. 2024

Materials

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As a key guarantee and cornerstone of building quality, the importance of deformation prediction for deep foundation pits cannot be ignored. However, the deformation data of deep foundation pits have the characteristics of nonlinearity and instability, which will increase the difficulty of deformation prediction. In response to this characteristic and the difficulty of traditional deformation prediction methods to excavate the correlation between data of different time spans, the advantages of variational mode decomposition (VMD) in processing non-stationary series and a gated cycle unit (GRU) in processing complex time series data are considered. A predictive model combining particle swarm optimization (PSO), variational mode decomposition, and a gated cyclic unit is proposed. Firstly, the VMD optimized by the PSO algorithm was used to decompose the original data and obtain the Internet Message Format (IMF). Secondly, the GRU model optimized by PSO was used to predict each IMF. Finally, the predicted value of each component was summed with equal weight to obtain the final predicted value. The case study results show that the average absolute errors of the PSO-GRU prediction model on the original sequence, EMD decomposition, and VMD decomposition data are 0.502 mm, 0.462 mm, and 0.127 mm, respectively. Compared with the prediction mean square errors of the LSTM, GRU, and PSO-LSTM prediction models, the PSO-GRU on the PTB0 data of VMD decomposition decreased by 62.76%, 75.99%, and 53.14%, respectively. The PTB04 data decreased by 70%, 85.17%, and 69.36%, respectively. In addition, compared to the PSO-LSTM model, it decreased by 8.57% in terms of the model time. When the prediction step size increased from three stages to five stages, the mean errors of the four prediction models on the original data, EMD decomposed data, and VMD decomposed data increased by 28.17%, 3.44%, and 14.24%, respectively. The data decomposed by VMD are more conducive to model prediction and can effectively improve the accuracy of model prediction. An increase in the prediction step size will reduce the accuracy of the deformation prediction. The PSO-VMD-GRU model constructed has the advantages of reliable accuracy and a wide application range, and can effectively guide the construction of foundation pit engineering.

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“…Cao et al 27 used fully integrated EMD to divide the one-dimensional ground subsidence data into multidimensional ones, and each component was predicted by the LSTM neural network and superimposed to obtain the final prediction results, which produced better prediction results. Luo et al 28 proposed a combined prediction model based on empirical mode decomposition (EMD), support vector regression (SVR) and wavelet neural network (WNN) called the EMD-SVR-WNN model. The EMD method was used to decompose structural monitoring data, and then the WNN and SVR methods were used to make predictions.…”

Section: Introductionmentioning

confidence: 99%

A novel combined intelligent algorithm prediction model for the tunnel surface settlement

Wang,

Dai,

Jia

et al. 2023

Sci Rep

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To ensure the safety and stability of the shield tunnel construction process, the ground settlement induced by the shield construction needs to be effectively predicted. In this paper, a prediction method combining empirical mode decomposition (EMD), chaotic adaptive sparrow search algorithm (CASSA), and extreme learning machine (ELM) is proposed. First, the EMD is used to decompose the settlement sequence into trend vectors and fluctuation vectors to fully extract the effective information of the sequence; Second, the sparrow search algorithm is improved by introducing Cubic chaotic mapping to initialize the population and adaptive factor to optimize the searcher’s position formula, and the chaotic adaptive sparrow search algorithm is proposed; Finally, the CASSA-ELM prediction model is constructed by using CASSA to find the optimal values of weights and thresholds in the extreme learning machine. The fluctuation components and trend components decomposed by EMD are predicted one by one, and the prediction results are superimposed and reconstructed to obtain the predicted final settlement. Taking a shield interval in Jiangsu, China as an example, the meta-heuristic algorithm-optimized ELM model improves the prediction accuracy by 10.70% compared with the traditional ELM model. The combined EMD-CASSA-ELM prediction model can greatly improve the accuracy and speed of surface settlement prediction, and provide a new means for safety monitoring in shield tunnel construction. Intelligent prediction methods can predict surface subsidence more automatically and quickly, becoming a new development trend.

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A Prediction Model of Structural Settlement Based on EMD‐SVR‐WNN

Cited by 10 publications

References 27 publications

Discovering the Research Topics on Construction Safety and Health Using Semi-Supervised Topic Modeling

Discovering the Research Topics on Construction Safety and Health Using Semi-Supervised Topic Modeling

Research on Deformation Prediction of VMD-GRU Deep Foundation Pit Based on PSO Optimization Parameters

A novel combined intelligent algorithm prediction model for the tunnel surface settlement

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